Class and classification
On the orthogonal distance to class subspaces for high-dimensional data classification
... The orthogonal distance from an instance to the subspace of a class is a key metric for pattern classification by the class subspace-based methods. There is a close relationship between the ...
30
A Unified View on Multi-class Support Vector Classification
... The LLW, RM, and ATS SVMs rely on a classification calibrated loss function. However, we have shown that a non-classification calibrated training loss function does not need to be a principal problem for an ...
32
A SURVEY ONE CLASS CLASSIFICATION USING ENSEMBLES METHOD
... Data classification is the categorization of data for its most effective and efficient ...Data classification is a two-step process: ...a classification algorithm builds the classifier by analyzing ...
5
Sentiment classification with concept drift and imbalanced class distributions
... In classification task, distribution classes of dataset may be unequal that is called imbalanced data problem, that learning algorithms are biased towards the majority classes ...skewed class distribution ...
8
Multi-class Protein Classification Using Adaptive Codes
... Protein classification is the prediction of a protein’s structural class from its primary sequence of amino ...structural class enables the selection of a template structure from the database, which ...
25
Boosted Classification Trees and Class Probability/Quantile Estimation
... in classification with unequal costs, as in medical problems where a false negative is often much more serious than a false ...the class label that minimizes the expected ...Alternatively, ...
31
Issues of Class Imbalance in Classification of Binary Data: A Review
... Handling classification issues of class imbalance data has gained attentions of researchers in the last few ...years. Class imbalance problem evolves when one of two classes has more sample than the ...
5
CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification.
... minority class is close to the average number of samples of the majority ...minority class is below the average number of samples of the five subclasses after class ...minority class using the ...
14
One-class Classification: An Approach to Handle Class Imbalance in Multimodal Biometric Authentication
... the classification performance of the extremely imbalanced biometric data ...impostor class, and thus, decreasing the degree of class imbalance and counteracting the effects of the ...
176
Active Learning of Multi-Class Classification Models from Ordered Class Sets
... Multi-class support vector machine (MCSVM) was first pro- posed by (Vapnik 1998; Weston et al. 1999). Basically, for a K-classification problem, MCSVM trains K one-vs-rest binary classifiers in one ...
8
Exploitation of Pairwise Class Distances for Ordinal Classification
... nal classification, they learn the projection from the input space onto the 1-dimensional latent space only indirectly as part of the overall model ...the class distribu- tion obtained from pairwise ...
55
Aspects of multi-class nearest hypersphere classification
... The following diagram in Figure 3.3 explains a classification tree. The root node contains the entire training set. We will now have a condition that can be true or false. This is called a Boolean condition. There ...
127
An improved random forest classifier for multi-class classification
... of classification accuracy of Random Forest algorithm for multi-class ...disease classification accuracy as ...for classification accuracy, F- measure and sensitivity values with 10-fold cross ...
8
Large Margin Hierarchical Classification with Mutually Exclusive Class Membership
... hierarchical classification, based on a new concept of structured functional and geometric margins defined for each node of the hierarchy, which differs from the concept of the loss-weighted margins in structured ...
28
Hierarchical ensemble classification: towards the classification of data collections that feature large numbers of class labels
... the class a single classifier has to learn ...binary classification but it can be applied with respect to the multi-class ...each class), the example is assigned to the class associated ...
293
Can I Trust My One-Class Classification?
... cover class of ...the class of interest, while training data for other classes is not ...one-class classification is fraught with uncertainty and full automatization is difficult, due to the ...
24
Optimising ECOC matrices in multi-class classification problems
... Multi-class classification deals with the problem of classifying an input into one of mul- tiple classes, given its input ...for classification and assign each movie cd to one genre. ...
80
Using Output Codes for Two-class Classification Problems
... In the previous chapters, we have discussed different encoding and decoding meth- ods for Error-Correcting Output Codes, and how to use a clustering approach to turn two-class problems into multi-class ...
118
Probability Estimates for Multi-class Classification by Pairwise Coupling
... Pairwise coupling is a popular multi-class classification method that combines all com- parisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both ...
31
Hyperparameter optimisation for improving classification under class imbalance
... the class overlap even ...the classification algorithms would perform better on datasets with low ...a classification problem is not only determined by the overlap between classes but also related to ...
7